I am an associate professor in the School of Information at the University of Michigan School.
NSF CAREER Award Recipient
Google Faculty Award Recipient
Facebook Faculty Award Recipient
NSF Algorithms in the Field Grant Recipient
NSF CCF Small Recipient (3x)
NSF RI Medium Recipient
Bo Li - former Post-Doc, now TT at UIUC
Yuqing Kong - former PhD student, now TT at Peking University
Fang-Yi Yu - former PhD Student/Post-Doc, now TT at George Mason University
Biaoshuai Tao - former PhD Student, now TT at SJTU
Noah Burrell - former PhD Student, now at Epistemix
Yichi Zhang - former PhD Student, now a Postdoc at Rutgers/DIMACS
Md Sanzeed Anwar - current PhD Student
Shengwei Xu - current PhD Student
Christian David Gamba Contrera - current PhD Student
Benchmarking LLMs' Judgments with No Gold Standard
S. Xu, Y. Lu, Y. Zhang, G. Schoenebeck, Y. Kong
ICLR '25, Arxiv
Eliciting Informative Text Evaluations with Large Language
Models
Y. Lu, S. Xu, Y. Zhang, Y. Kong, G. Schoenebeck
EC '24, PDF, Arxiv
Measurement Integrity in Peer Prediction: A Peer Assessment Case Study
N. Burrell, G. Schoenebeck
EC '23, Arxiv
Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences
G. Schoenebeck, b. Tao
NeurIPs, 2021, Arxiv
Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach
G. Schoenebeck, F. Yu.
ITCS 2021, Arxiv.
Benchmarking LLMs' Judgments with No Gold Standard
S. Xu, Y. Lu, Y. Zhang, G. Schoenebeck, Y. Kong
ICLR '25, Arxiv
Recommendation and Temptation
S. Anwar, P. Dhillon, G. Schoenebeck
arXiv '24
Strong Equilibria in Bayesian Games with Bounded Group Size
Q. Han, G. Schoenebeck, B. Tao, L. Xia
WWW '25 (to appear)
Eliciting Informative Text Evaluations with Large Language
Models
Y. Lu, S. Xu, Y. Zhang, Y. Kong, G. Schoenebeck
EC '24, PDF, Arxiv
Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanisms
S. Xu, Y. Zhang, P. Resnick, G. Schoenebeck
WWW '24, arXiv
Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns
S. Anwar and G. Schoenebeck, P. Dhillon
WWW '24, arXiv
Exit Ripple Effects: Understanding the Disruption of Socialization Networks Following Employee Departures
D. Gamba, Y. Yu, Y. Yuan, G. Schoenebeck, D. Romero
WWW '24, arXiv
Eliciting Honest Information From Authors Using Sequential Review
Y. Zhang, G. Schoenebeck, W. Su
AAAI '24, arXiv
Testing Conventional Wisdom (of the Crowd)
N. Burrell, G. Schoenebeck
UAI '23
Measurement Integrity in Peer Prediction: A Peer Assessment Case Study
N. Burrell, G. Schoenebeck
EC '23, Arxiv
The Wisdom of Strategic Voting
Q. Han, G. Schoenebeck, B. Tao, L. Xia
EC '23, Arxiv
High-Effort Crowds: Limited Liability via Tournaments
Y. Zhang, G. Schoenebeck
WWW'23
Multitask Peer Prediction With Task-dependent Strategies
Y. Zhang, G. Schoenebeck
WWW'23
Two Strongly Truthful Mechanisms for Three Heterogeneous Agents Answering One Question
G. Schoenebeck, F. Yu
TEAC, 2023,
Wine 2020,
pdf
False Consensus, Information Theory, and Prediction Markets.
Y. Kong, G. Schoenebeck
ITCS '23 , arXiv
A System-Level Analysis of Conference Peer Review.
Y. Zhang, F. Yu, G. Schoenebeck, and D. Kempe
EC '22.
Optimal Local Bayesian Differential Privacy Over Markov Chains.
D. Chakrabarti, J. Gao, A. Saraf, G. Schoenebeck, and F. Yu
AAMAS '22 (extended abstract), arXiv.
Bayesian Persuasion in Sequential Trials.
S. Su, V. Subramanian, and G. Schoenebeck
WINE '21, arXiv.
Adaptive Greedy Versus Non-adaptive Greedy for Influence Maximization
W. Chen, B. Peng, G. Schoenebeck, B. Tao
JAIR '22, AAAI '20, arXiv
BONUS! Maximizing Surprise Labels
Z. Huang, Y. Kong, T. X. Liu, G. Schoenebeck, S. Xu
WWW '22, Arxiv
Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences
G. Schoenebeck, b. Tao
NeurIPs, 2021, Arxiv, 2021
SURPRISE! and When to Schedule It
Z. Huang, S. Xu, Y. Shan, Y. Lu, Y. Kong, T. X. Liu, G. Schoenebeck
IJCAI '21, Arxiv
Survey Equivalence: A Procedure for Measuring Classifier Accuracy Against Human Labels
P. Resnick, Y. Kong, G. Schoenebeck, T. Weninger
Arxiv, 2021
Information Elicitation from Rowdy Crowds
G. Schoenebeck, F. Yu, Y. Zhang
WWW '21
Timely Information from Prediction Markets
G. Schoenebeck, C. Yu, F. Yu
Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach
G. Schoenebeck, F. Yu.
ITCS 2021, Arxiv
Relaxing Common Belief for Social Networks
N. Burrell, G. Schoenebeck
ITCS 2021, Arxiv
Escaping Saddle Points in Constant Dimensional Spaces: An Agent-based Modeling Perspective
G. Schoenebeck, F. Yu
EC' 2020, pdf
Limitations of greed: Influence maximization in undirected networks re-visited
G. Schoenebeck, B. Tao, F. Yu
AAMAS '20, arXiv
Information Elicitation Mechanisms for Statistical Estimation
Y. Kong, G. Schoenebeck, B. Tao, F. Yu
AAAI '20, pdf
Influence Maximization on Undirected Graphs: Towards Closing the (1-1/e) Gap
G. Schoenebeck, B. Tao
EC '19,
Video Presentation,
TEAC '20
Outsourcing computation: the minimal refereed mechanism.
Y. Kong, C. Peikert, G. Schoenebeck, B. Tao
Wine '19, arXiv
Think globally, act locally: On the optimal seeding for nonsubmodular influence maximization.
G. Schoenebeck, B. Tao, F. Yu
Approx/Random '19, arXiv
An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
Y. Kong, G. Schoenebeck.
TEAC '19,
Arxiv
Complex Contagions in Charitable Donations
J. Gao, G. Ghsemisefeh and J. Jones, G. Schoenebeck.
SocArXiv '19.
Beyond Worst-Case (In)approximability of Nonsubmodular Influence Maximization
G. Schoenebeck, B. Tao
ToCT '19,
Wine '17,
arXiv '17
Think Globally, Act Locally: On the Optimal Seeding for Nonsubmodular Influence Maximization
G. Schoenebeck, B. Tao, F. Yu
Approx/Random '19
The Volatility of Weak Ties: Co-evolution of Selection and Influence
in Social Networks
J. Gao, G. Schoenebeck, F. Yu
AAMAS '19,
pdf
Outsourcing Computation: the Minimal Refereed Mechanism
Y. Kong, C. Peikert, G. Schoenebeck, B. Tao,
Wine'19, arXiv
Social learning with questions
S. Su, V. G. Subramanian, G. Schoenebeck
NetEcon '19, arXiv
Water from Two Rocks: Maximizing the Mutual Information
Y. Kong, G. Schoenebeck
Eliciting Expertise without Verification
Y. Kong, G. Schoenebeck
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
X. Ma, B. Li, Y. Wang, S. M. Erfani, S. Wijewickrema, M. E. Houle, G. Schoenebeck, D. Song, J. Bailey
ICLR '18, arXiv '18
Consensus of Interacting Particle Systems on Erdos-Renyi Graphs
G. Schoenebeck, F. Yu
SODA '18, pdf
Optimizing Bayesian Information Revelation Strategy in Prediction Markets: the Alice Bob Alice Case
Y. Kong, G. Schoenebeck.
ITCS '18
Equilibrium Selection in Information Elicitation without Verification via Information Monotonicity
Y. Kong, G. Schoenebeck..
ITCS' 18, Arxiv '16
Contention-Aware Lock Scheduling for Transactional Databases
B. Tian, J. Huang, B. Mozafari, G. Schoenebeck
VLDB'18
Don't Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence Maximization
R. Angell, G. Schoenebeck
WINE'17, arXiv '16
Cascades and Myopic Routing in Nonhomogeneous Kleinbergs Small World Model
J. Gao, G. Schoenebeck, F. Yu
WINE '17
A Top-Down Approach to Achieving Performance Predictability in Database Systems
J. Huang, B. Mozafari, G. Schoenebeck, T. Wenisch
SIGMOD '17
Engineering Agreement:The Naming Game with Asymmetric and Heterogeneous Agents
J. Gao, B. Li, G. Schoenebeck, F. Yu
AAAI '17
How Complex Contagions Spread Quickly in Preferential Attachment Models and Other Time-Evolving Networks
R.Ebrahimi, J. Gao, G. Ghasemiesfeh, G. Schoenebeck
IEEE Transactions on Network Science and Engineering '17, arXiv '14
Sybil Detection Using Latent Network Structure
A. Snook, G. Schoenebeck, F. Yu.
EC '16
General Threshold Model for Social Cascades: Analysis and Simulations
J. Gao, G. Ghasemiesfeh, G. Schoenebeck, F. Yu
EC '16
Complex Contagions on Configuration Model Graphs with a Power-Law Degree Distribution
G. Schoenebeck, F. Yu
WINE '16
Putting Peer Prediction Under the Micro(economic)scope and Making Truth-telling Focal
Y. Kong, K. Ligett, G. Schoenebeck.
WINE '16, Arxiv '15
Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases
J. Huang, B. Mozafari, G. Schoenebeck, T. Wenisch
arXiv'16
A Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
Y. Kong, G. Schoenebeck..
Arxiv '15
Complex Contagions in Kleinberg's Small World Model
R. Ebrahimi, J. Gao, G. Ghasemiesfeh, G. Schoenebeck
ITCS '15
Buying Private Data without Verification
A. Ghosh, K. Ligett, A. Roth, G. Schoenebeck.
EC '14
Characterizing Strategic Cascades on Networks
T. Martin, G. Schoenebeck, M. Wellman
EC '14
Graph Isomorphism and the Lasserre Hierarchy
P. Codenotti, G. Schoenebeck, A. Snook
arXiv '14
Better Approximation Algorithms for the Graph Diameter.
S. Chechik, D. H. Larkin, L. Roditty, G. Schoenebeck, R. E. Tarjan, V. V. Williams
SODA '14
Potential Networks, Contagious Communities, and Social Network Structure.
G. Schoenebeck
WWW '13
Conducting Truthful Surveys, Cheaply
A. Roth, G. Schoenebeck.
EC '12
Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach
S. Arora, R. Ge, S. Sachdeva, G. Schoenebeck
EC '12
Social Learning in a Changing World
R. Frongillo, G. Schoenebeck, O. Tamuz
Wine '11
General Hardness Amplification of Predicates and Puzzles
T. Hollenstein, G. Schoenebeck
TCC '11
Constrained Non-monotone Submodular Maximization: Offline and Secretary Algoritms.
A. Gupta, A. Roth. G. Schoenebeck, K. Talwar.
WINE '10
The Limitations of Linear and Semidefinite Programs
G. Schoenebeck
PhD Thesis, 2010
Optimal Testing of Reed-Muller Codes
A. Bhattacharyya, S. Kopparty, G. Schoenebeck, M. Sudan, D. Zuckerman
FOCS '10.
Detecting Spam in a Twitter Network.
S. Yardi, D. Romero, G. Schoenebeck. d. boyd.
First Monday '10
Reaching Consensus on Social Networks
E. Mossel, G. Schoenebeck
ICS '10.
On the Complexity of Nash Equilibria of Action-Graph Games
C. Daskalakis, G. Schoenebeck, G. Valiant, P. Valiant
Soda '09.
Linear Level Lasserre Lower Bounds for Certain k-CSPs
G. Schoenebeck.
FOCS '08
Tight Integrality Gaps for Lovasz-Schrijver LP Relaxations of Vertex Cover and Max Cut
G. Schoenebeck, L. Trevisan, M. Tulsiani.
STOC '07
A Linear Round Lower Bound for Lovasz-Schrijver SDP Relaxations of Vertex Cover
G. Schoenebeck, L. Trevisan, M. Tulsiani.
CCC '07
Chora: Expert-based Peer-to-peer web search
H. Gylfason, O. Khan, G. Schoenebeck
AP2PC workshop at AAMAS '06.
The computational Complexity of Concisely Represented Games
G. Schoenebeck, S. Vadhan.
EC '06. ACM Transactions on Computation Theory 2012.
GrowRange: Anytime VCG-Based Mechanisms
D. Parkes, G. Schoenebeck.
AAAI '04.
Fall 2023
SI: 670: Applied Machine Learning
Winter 2023
EECS 547 / SI: 652: Incentives and Strategic Behavior in Computational Systems
SI 602: Mathematical Foundations for Data Science
Fall 2022
SI: 670: Applied Machine Learning
Winter 2021
SIADS 502: Math Methods for Data Science
SIADS 521: Visual Exploration of Data
Fall 2020
EECS 547 / SI: 652: Incentives and Strategic Behavior in Computational Systems
SI: 670: Applied Machine Learning
SIADS 502: Math Methods for Data Science
Winter 2020
SIADS 502: Math Methods for Data Science
SIADS 521: Visual Exploration of Data
Fall 2019
EECS 547 / SI: 652: Electronic Commerce (about algorithmic game theory)
SI: 670: Applied Machine Learning
Fall 2017
EECS 547 / SI: 652: Electronic Commerce (about algorithmic game theory) .
Winter 2017
EECS 376 Foundations of Computing
Fall 2015
EECS 598-06 Randomness and Computation
Winter 2015
EECS 376 Foundations of Computing
Fall 2014
EECS 574 Computational Complexity Theory
Fall 2013
EECS 574 Computational Complexity Theory
Winter 2013
EECS 203 Discrete Math
Fall 2012
EECS 598-06 Social Networks: Reasoning about Structure and Processes This class looked at social networks research and how a theoretical computer science prospective both brings new questions and gains additional insights into this growing body of research. Schedule and readings on the website.
New Jersey Governor's School: The Math Behind the Maching, Summer 2012
New Jersey Governor's School: The Math Behind the Maching, Summer 2011
3341 North Quad
501 State St.
University of Michigan
Ann Arbor, MI 48109-2121
Phone: (734)647-4712
Email:
I was born in Green Bay, WI and moved to Wichita, KS when I was nine. I attended Harvard University, graduating with highest honors in mathematics. Afterwards, I attended Oxford University as the von Clemm fellow and studied theology. I received my PhD from UC Berkeley in computer science where I was advised by Luca Trevisan. Subsequently I was the Simons Foundation Postdoctoral Research Fellow in Theoretical Computer Science at Princeton University.